Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine

arXiv:2605.05706v1 Announce Type: new Abstract: Estimating individualized treatment effects from longitudinal observational data is central to data-driven medicine, yet existing methods face a fundamental limitation: reducing confounding bias often suppresses clinically informative heterogeneity,...